TwinPilot
TwinPilot is a machine learning model available remotely 24/7 that supports decision-making and control of machines, installations and industrial processes. We begin by creating a Digital Twin of the system, representing its behavior and the relationships between variables. We then use it to train an ML model, including through reinforcement learning (RL), to prepare it to make decisions under different operating conditions. After deployment, the model keeps learning: it uses new data and experience to improve how it responds to changes in the process. It can consider hundreds of factors simultaneously and update decisions as often as every minute, adjusting setpoints to the current situation, production goals and technical constraints. Remote operation provides round-the-clock access to the model and allows it to evolve as the plant’s needs change. TwinPilot works with existing PLCs, which handle basic control and safety functions, while the model’s decisions are implemented within agreed operating limits.
Applications
- Real-time control of nonlinear systems with multiple interdependent variables.
- Optimizing electricity consumption in cooling, HVAC, compressors and industrial processes, as well as energy use in boiler plants.
- Adjusting setpoints to ambient temperature, weather conditions, machine loads and current production demand.
- Recognizing recurring production cycles and operating patterns to anticipate demand and adjust the installation’s operation in advance.
- Coordinating interdependent equipment while taking process quality, throughput and technical constraints into account.
- Optimizing a single machine or a larger installation after integration with its control system.